Development and Evaluation of an Adaptive Penguin-Improved LSTM Model Integrating Natural Language Processing for Early Prediction of Pregnancy Syndrome Risks.

backgroundEarly prediction of pregnancy syndromes such as preeclampsia, gestational diabetes mellitus (GDM), and preterm birth is critical for improving maternal and fetal outcomes. Traditional risk assessment tools rely primarily on structured clinical data and often fail to fully utilize the rich unstructured textual information contained in electronic health records (EHRs).

objectiveThis study aimed to develop and evaluate an Adaptive Penguin-Improved Long Short-Term Memory (AP-ILSTM) model that integrates natural language processing (NLP) of clinical narratives with structured longitudinal data for predicting the risk of preeclampsia, GDM, and preterm birth.

methodsThis retrospective observational study used de-identified EHR data from 1248 consecutive pregnancies at a single regional hospital in China between April 2024 and April 2025. Structured clinical variables and unstructured free-text clinical notes were preprocessed using standard NLP techniques, including tokenization, TF-IDF weighting, and word embeddings. An Improved Long Short-Term Memory (ILSTM) network with dropout regularization was optimized using the Adaptive Penguin algorithm with Gaussian exploration. The dataset was stratified into training (70%), validation (15%), and independent test (15%) sets. Model performance was assessed using accuracy, sensitivity (recall), positive predictive value (precision), F1-score, specificity, negative predictive value, ROC-AUC, and mean absolute error, and compared against baseline machine learning models.

resultsOn the independent test set (n = 188), the AP-ILSTM model achieved an accuracy of 98.85%, sensitivity of 98.69%, positive predictive value of 98.75%, F1-score of 98.72%, and ROC-AUC of 0.993, with a mean absolute error of 4.05. It outperformed all baseline classifiers (XGBoost, CatBoost, KNN) and regression models. Clinical narrative embeddings ranked as the second most important predictor after blood pressure trends.

conclusionsThe AP-ILSTM model, by effectively combining structured data with NLP-derived features from clinical narratives and employing adaptive optimization, demonstrated superior performance for early multi-syndrome pregnancy risk prediction. This multimodal temporal approach shows strong potential to support clinical decision-making, although external validation and prospective implementation studies are required before clinical adoption.